DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 10-13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 10 recites the limitation "2D CNN" in line 1. There is insufficient antecedent basis for this limitation in the claim.
Claims 11-13 depend from claim 10 and incorporate the same indefinite language as recited by claim 10.
Claim 12 further recites the limitation "2D CNN" in line 2. There is insufficient antecedent basis for this limitation in the claim (based on improper antecedent basis in claim 10).
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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The present application is rejected for double patenting in view of the following U.S. Patents (rationale set forth below):
U.S. Patent No. 12,100,093
U.S. Patent 11,694,387
U.S. Patent No. 11,410,376
Claims 1-18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-18 of U.S. Patent No. 12,100,093. Although the claims at issue are not identical, they are not patentably distinct from each other because although the conflicting claims are not identical, they are not patentably distinct from each other because the claims of the present application are anticipated by the claims of U.S. Patent No. 12,100,093.
The following tables illustrate the conflicting claim pairs:
Present App.
1
2
3
4
5
6
7
8
9
Patent No. 12,100,093
1
2
3
4
5
6
7
8
9
Present App.
10
11
12
13
14
15
16
17
18
Patent No. 12,100,093
10
11
12
13
14
15
16
17
18
The following table illustrates the limitations of claim 1 of the present application when compared against claim 1 of U.S. Patent No. 12,100,093:
Present Application – Claim 1
U.S. Patent 12,100,093 B2 - Claim 1
1. A method of generating a three-dimensional (3D) reconstruction of a scene from multiview images, the method comprising:
1. A method of generating a three-dimensional (3D) reconstruction of a scene from multiview images, the method comprising:
obtaining a sequence of frames of images; extracting features from the sequence of frames of images using a two-dimensional convolutional neural network (2D CNN);
back-projecting features from each frame of a sequence of frames into a 3D voxel volume wherein each pixel of the 3D voxel volume is mapped to a ray in the 3D voxel volume;
back-projecting the features from each frame into a 3D voxel volume wherein each pixel of the 3D voxel volume is mapped to a ray in the 3D voxel volume;
passing the 3D voxel volume through a 3D convolutional neural network (3D CNN) having an encoder-decoder to refine the features in the 3D voxel volume and regress output truncated signed distance function (TSDF) values at each voxel of the 3D voxel volume; and
passing the 3D voxel volume through a 3D convolutional neural network (3D CNN) having an encoder-decoder to refine the features in the 3D voxel volume and regress output truncated signed distance function (TSDF) values at each voxel of the 3D voxel volume; and
after passing the 3D voxel volume through all layers of the 3D CNN, passing the refined features in the 3D voxel volume and TSDF values at each voxel of the 3D voxel volume through a batch normalization (batchnorm) function and a rectified linear unit (reLU) function,
after passing the 3D voxel volume through all layers of the 3D CNN, passing the refined features in the 3D voxel volume and TSDF values at each voxel of the 3D voxel volume through a batch normalization (batchnorm) function and a rectified linear unit (reLU) function,
wherein the 3D reconstruction is generated without the use of depth data from depth sensors.
wherein the 3D reconstruction is generated without the use of depth data from depth sensors.
The following table illustrates the limitations of claim 14 of the present application when compared against claim 14 of U.S. Patent No. 12,100,093:
Present Application – Claim 14
U.S. Patent 12,100,093 B2 - Claim 14
14. A cross reality system, comprising:
14. A cross reality system, comprising:
A head-mounted display device having a display system;
A head-mounted display device having a display system;
a computing system in operable communication with the head-mounted display device;
a computing system in operable communication with the head-mounted display device;
A plurality of camera sensors in operation communication with the computing system;
a plurality of camera sensors in operable communication with the computing system;
Wherein the computing system is configured to generate a three-dimensional (3D) reconstruction of a scene from a sequence of frames of images captured by the camera sensors by a process comprising:
wherein the computing system is configured to generate a three-dimensional (3D) reconstruction of a scene from a sequence of frames of images captured by the camera sensors by a process comprising:
obtaining a sequence of frames of images; extracting features from the sequence of frames of images using a two-dimensional convolutional neural network (2D CNN);
back-projecting features from each frame of a sequence of frames into a 3D voxel volume wherein each pixel of the 3D voxel volume is mapped to a ray in the 3D voxel volume;
back-projecting the features from each frame into a 3D voxel volume wherein each pixel of the 3D voxel volume is mapped to a ray in the 3D voxel volume;
passing the 3D voxel volume through a 3D convolutional neural network (3D CNN) having an encoder-decoder to refine the features in the 3D voxel volume and regress output truncated signed distance function (TSDF) values at each voxel of the 3D voxel volume; and
passing the 3D voxel volume through a 3D convolutional neural network (3D CNN) having an encoder-decoder to refine the features in the 3D voxel volume and regress output truncated signed distance function (TSDF) values at each voxel of the 3D voxel volume; and
after passing the 3D voxel volume through all layers of the 3D CNN, passing the refined features in the 3D voxel volume and TSDF values at each voxel of the 3D voxel volume through a batch normalization (batchnorm) function and a rectified linear unit (reLU) function,
after passing the 3D voxel volume through all layers of the 3D CNN, passing the refined features in the 3D voxel volume and TSDF values at each voxel of the 3D voxel volume through a batch normalization (batchnorm) function and a rectified linear unit (reLU) function,
wherein the 3D reconstruction is generated without the use of depth data from depth sensors.
wherein the 3D reconstruction is generated without the use of depth data from depth sensors.
Claims 1 and 14 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 5 and 18 of U.S. Patent No. 11,410,376 in view of Xie et al., “Pix2Vox: Content-aware 3D Reconstruction from Single and Multi-view Images” – NPL reference cited by applicant in IDS filed 8/28/2024. Although the claims at issue are not identical, they are not patentably distinct from each other because although the conflicting claims are not identical, they are not patentably distinct from each other because the claims of the present application are obvious in view of the claims of U.S. Patent No. 11,410,376.
The following tables illustrate the conflicting claim pairs:
Present App.
1
14
Patent No. 11,410,376
5
18
The following table illustrates the limitations of claim 1 of the present application when compared against claim 5 of U.S. Patent No. 11,410,376:
Present Application – Claim 1
U.S. Patent 11,410,376 - Claim 5
1. A method of generating a three-dimensional (3D) reconstruction of a scene from multiview images, the method comprising:
{from parent claim}. A method of generating a three-dimensional (3D) reconstruction of a scene from multiview images, the method comprising:
obtaining a sequence of frames of red green blue (RGB) images;
extracting features from the sequence of frames of RGB images using a two-dimensional convolutional neural network (2D CNN);
back-projecting features from each frame of a sequence of frames into a 3D voxel volume wherein each pixel of the 3D voxel volume is mapped to a ray in the 3D voxel volume;
back-projecting the features from each frame using known camera intrinsics and extrinsics into a 3D voxel volume wherein each pixel of the voxel volume is mapped to a ray in the voxel volume;
fusing/accumulating features from each frame into the 3D voxel volume;
passing the 3D voxel volume through a 3D convolutional neural network (3D CNN) having an encoder-decoder to refine the features in the 3D voxel volume and regress output truncated signed distance function (TSDF) values at each voxel of the 3D voxel volume; and
passing the 3D voxel volume through a 3D convolutional neural network (3D CNN) having an encoder-decoder to refine the features in the 3D voxel volume and regress output truncated signed distance function (TSDF) values at each voxel of the 3D voxel volume.
after passing the 3D voxel volume through all layers of the 3D CNN, passing the refined features in the 3D voxel volume and TSDF values at each voxel of the 3D voxel volume through a batch normalization (batchnorm) function and a rectified linear unit (reLU) function,
{From claim 5}
after passing the 3D voxel volume through all layers of the 3D CNN, passing the refined features in the 3D voxel volume and TSDF values at each voxel of the 3D voxel volume through a batch normalization (batchnorm) Junction and a rectified linear unit (reLU) function.
wherein the 3D reconstruction is generated without the use of depth data from depth sensors.
As illustrated above, the only limitation of claim 1 of the present application not taught by claim 5 of patent 11,410,376 is, “wherein the 3D reconstruction is generated without the use of depth data from depth sensors.”
However, Xie teachs:
A method of generating 3D reconstruction of a scene wherein the 3D reconstruction is generated without the use of depth data from depth sensors (Xie Abstract: single view mor multi-view 3D reconstruction; Section 4.8 Discussion, ¶2: “Pix2Vox recovers the 3D shape of an object without known camera parameters.”)
One of ordinary skill in the art would have found it obvious, before the effective filing
date of the claimed invention and with a reasonable expectation of success, to modify the claimed method for generating 3D reconstruction of a scene of U.S. Patent No. 11,410,376 using the technique of 3D reconstruction without use of depth data from depth sensors provided by Xie, using known electronic interfacing and programming techniques. The modification would result in an improved 3D reconstruction by requiring less specialized equipment (namely depth sensors) to generate 3D reconstruction from a scene, for easier and less expensive
implementation
The following table illustrates the limitations of claim 14 of the present application when compared against claim 18 of U.S. Patent No. 11,410,376:
Present Application – Claim 14
U.S. Patent 11,410,376 - Claim 18
14. A cross reality system, comprising:
{from parent claim 14} A cross reality system, comprising:
A head-mounted display device having a display system;
a head-mounted display device having a display system;
a computing system in operable communication with the head-mounted display device;
a computing system in operable communication with the head-mounted display;
A plurality of camera sensors in operation communication with the computing system;
a plurality of camera sensors in operable communication with the computing system;
Wherein the computing system is configured to generate a three-dimensional (3D) reconstruction of a scene from a sequence of frames of images captured by the camera sensors by a process comprising:
wherein the computing system is configured to generate a three-dimensional (3D) reconstruction of the scene from a sequence of frames of RGB images captured by the camera sensors by a process comprising:
obtaining a sequence of a frames of red green blue (RGB) images of a scene within a field of view of the camera sensors from the camera sensors; extracting features from the sequence of frames of RGB images using a two-dimensional convolutional neural network (2D CNN);
back-projecting features from each frame of a sequence of frames into a 3D voxel volume wherein each pixel of the 3D voxel volume is mapped to a ray in the 3D voxel volume;
back-projecting the features from each frame using known camera intrinsics and extrinsics into a 3D voxel volume wherein each pixel of the voxel volume is mapped to a ray in the voxel volume; fusing the features from each frame into the 3D voxel volume;
passing the 3D voxel volume through a 3D convolutional neural network (3D CNN) having an encoder-decoder to refine the features in the 3D voxel volume and regress output truncated signed distance function (TSDF) values at each voxel of the 3D voxel volume; and
passing the 3D voxel volume through a 3D convolutional neural network (3D CNN) having an encoder-decoder to refine the features in the 3D voxel volume and regress output truncated signed distance function (TSDF) values at each voxel of the 3D voxel volume.
after passing the 3D voxel volume through all layers of the 3D CNN, passing the refined features in the 3D voxel volume and TSDF values at each voxel of the 3D voxel volume through a batch normalization (batchnorm) function and a rectified linear unit (reLU) function,
[From claim 5]
after passing the 3D voxel volume through all layers of the 3D CNN, passing the refined features in the 3D voxel volume and TSDF values at each voxel of the 3D voxel volume through a batch normalization (batchnorm) Junction and a rectified linear unit (reLU) function.
wherein the 3D reconstruction is generated without the use of depth data from depth sensors.
As illustrated above, the only limitation of claim 14 of the present application not taught by claim 18 of patent 11,410,376 is, “wherein the 3D reconstruction is generated without the use of depth data from depth sensors.”
However, Xie teaches:
A method of generating 3D reconstruction of a scene wherein the 3D reconstruction is generated without the use of depth data from depth sensors (Xie Abstract: single view mor multi-view 3D reconstruction; Section 4.8 Discussion, ¶2: “Pix2Vox recovers the 3D shape of an object without known camera parameters.”)
One of ordinary skill in the art would have found it obvious, before the effective filing
date of the claimed invention and with a reasonable expectation of success, to modify the claimed method for generating 3D reconstruction of a scene of U.S. Patent No. 11,410,376 using the technique of 3D reconstruction without use of depth data from depth sensors provided by Xie, using known electronic interfacing and programming techniques. The modification would result in an improved 3D reconstruction by requiring less specialized equipment (namely depth sensors) to generate 3D reconstruction from a scene, for easier and less expensive
implementation
Claims 1, 6, 7, 14, 17 and 18 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 6-8 and 18-20 of U.S. Patent No. 11,694,387. Although the claims at issue are not identical, they are not patentably distinct from each other because although the conflicting claims are not identical, they are not patentably distinct from each other because the claims of the present application are obvious in view of the claims of U.S. Patent No. 11,694,387
The following tables illustrate the conflicting claim pairs:
Present App.
1
6
7
14
17
18
Patent No. 11,694,387
6
7
8
18
19
20
The following table illustrates the limitations of claim 1 of the present application when compared against claim 6 of U.S. Patent No. 11,694,387:
Present Application – Claim 1
U.S. Patent 11,694,387 - Claim 6
1. A method of generating a three-dimensional (3D) reconstruction of a scene from multiview images, the method comprising:
6. A method of generating a three-dimensional (3D) reconstruction of a scene from multiview images, the method comprising:
obtaining a sequence of frames of red green blue (RGB) images;
extracting features from the sequence of frames of RGB images using a two-dimensional convolutional neural network (2D CNN);
back-projecting features from each frame of a sequence of frames into a 3D voxel volume wherein each pixel of the 3D voxel volume is mapped to a ray in the 3D voxel volume;
back-projecting the features from each frame using known camera intrinsics and extrinsics into a 3D voxel volume wherein each pixel of the voxel volume is mapped to a ray in the voxel volume; and
passing the 3D voxel volume through a 3D convolutional neural network (3D CNN) having an encoder-decoder to refine the features in the 3D voxel volume and regress output truncated signed distance function (TSDF) values at each voxel of the 3D voxel volume; and
passing the 3D voxel volume through a 3D convolutional neural network (3D CNN) having an encoder-decoder to refine the features in the 3D voxel volume and regress output truncated signed distance function (TSDF) values at each voxel of the 3D voxel volume; and
after passing the 3D voxel volume through all layers of the 3D CNN, passing the refined features in the 3D voxel volume and TSDF values at each voxel of the 3D voxel volume through a batch normalization (batchnorm) function and a rectified linear unit (reLU) function,
after passing the 3D voxel volume through all layers of the 3D CNN, passing the refined features in the 3D voxel volume and TSDF values at each voxel of the 3D voxel volume through a batch normalization (batchnorm) function and a rectified linear unit (reLU) function.
wherein the 3D reconstruction is generated without the use of depth data from depth sensors.
wherein the 3D reconstruction is generated without the use of depth data from depth sensors.
The following table illustrates the limitations of claim 14 of the present application when compared against claim 18 of U.S. Patent No. 11,694,387:
Present Application – Claim 14
U.S. Patent 11,694,387 - Claim 18
14. A cross reality system, comprising:
18. A cross reality system, comprising:
A head-mounted display device having a display system;
a head-mounted display device having a display system;
a computing system in operable communication with the head-mounted display device;
a computing system in operable communication with the head-mounted display;
A plurality of camera sensors in operation communication with the computing system;
a plurality of camera sensors in operable communication with the computing system;
Wherein the computing system is configured to generate a three-dimensional (3D) reconstruction of a scene from a sequence of frames of images captured by the camera sensors by a process comprising:
wherein the computing system is configured to generate a three-dimensional (3D) reconstruction of the scene from a sequence of frames of RGB images captured by the camera sensors by a process comprising:
obtaining a sequence of a frames of red green blue (RGB) images of a scene within a field of view of the camera sensors from the camera sensors; extracting features from the sequence of frames of RGB images using a two-dimensional convolutional neural network (2D CNN);
back-projecting features from each frame of a sequence of frames into a 3D voxel volume wherein each pixel of the 3D voxel volume is mapped to a ray in the 3D voxel volume;
back-projecting the features from each frame using known camera intrinsics and extrinsics into a 3D voxel volume wherein each pixel of the voxel volume is mapped to a ray in the voxel volume; and
passing the 3D voxel volume through a 3D convolutional neural network (3D CNN) having an encoder-decoder to refine the features in the 3D voxel volume and regress output truncated signed distance function (TSDF) values at each voxel of the 3D voxel volume; and
passing the 3D voxel volume through a 3D convolutional neural network (3D CNN) having an encoder-decoder to refine the features in the 3D voxel volume and regress output truncated signed distance function (TSDF) values at each voxel of the 3D voxel volume; and
after passing the 3D voxel volume through all layers of the 3D CNN, passing the refined features in the 3D voxel volume and TSDF values at each voxel of the 3D voxel volume through a batch normalization (batchnorm) function and a rectified linear unit (reLU) function,
after passing the 3D voxel volume through all layers of the 3D convolutional encoder-decoder, passing the refined features in the 3D voxel volume and TSDF values at each voxel of the 3D voxel volume through a batch normalization (batchnorm) function and a rectified linear unit (reLU) function.
wherein the 3D reconstruction is generated without the use of depth data from depth sensors.
wherein the 3D reconstruction is generated without the use of depth data from depth sensors.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM A BEUTEL whose telephone number is (571)272-3132. The examiner can normally be reached Monday-Friday 9:00 AM - 5:00 PM (EST).
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/WILLIAM A BEUTEL/Primary Examiner, Art Unit 2616